Method and system for thermodynamic analysis of wet air based on coordinate mapping and multi-source iteration

Abstract

This application provides a method and system for humid air thermodynamic analysis based on coordinate mapping and multi-source iteration, relating to the fields of computer-aided design and industrial software technology. The method includes: receiving a set of humid air state parameters; invoking a target evaluation strategy in a humid air thermodynamic physical model according to the combination type of the humid air state parameters; iterating or directly calculating a set of humid air state parameters based on the target evaluation strategy; mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity diagram; and performing an air handling process simulation based on the humid air thermodynamic physical model within the enthalpy-humidity diagram to obtain a comprehensive thermodynamic analysis result. This method can improve the calculation accuracy and engineering reliability of the comprehensive thermodynamic analysis result.

Description

Technical Field

[0001] This application relates to the fields of computer-aided design and industrial software technology, specifically to a method and system for humid air thermodynamic analysis based on coordinate mapping and multi-source iteration. Background Technology

[0002] In fields such as HVAC system design, meteorological analysis, environmental control for precision instruments, and agricultural drying, accurately understanding the thermodynamic state of moist air is crucial. Moist air, as a binary non-ideal mixture of dry air and water vapor, exhibits complex nonlinear coupling relationships among its state parameters (such as dry-bulb temperature, moisture content, and enthalpy). Traditional analytical methods primarily rely on psychrometric charts or consulting thermodynamic property tables.

[0003] Lookup table methods rely on limited discrete state point data, typically calculated under standard atmospheric pressure. When dealing with non-standard pressures (such as high-altitude areas), extreme temperature and humidity conditions, or when high-precision calculations are required, interpolation introduces significant errors and cannot guarantee the continuity and self-consistency of the calculation results from the underlying physical model. Therefore, existing techniques suffer from the inability to accurately describe the complex nonlinear and implicit functional relationships between humid air parameters, leading to low calculation accuracy. Summary of the Invention

[0004] Based on this, this application provides a method and system for humid air thermodynamic analysis based on coordinate mapping and multi-source iteration, which can improve the calculation accuracy and engineering reliability of comprehensive thermodynamic analysis results.

[0005] In a first aspect, this application provides a method for thermodynamic analysis of moist air based on coordinate mapping and multi-source iteration, including:

[0006] A set of humid air state parameters is received. Based on the combination type of the humid air state parameters, a target evaluation strategy in the humid air thermodynamic physical model is invoked. The humid air state parameter set is obtained through iterative or direct calculation based on the target evaluation strategy. The target evaluation strategy is the evaluation strategy corresponding to the combination type of the humid air state parameters. The humid air state parameter set is mapped from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map. The enthalpy-humidity map includes state points and contour lines. Based on the enthalpy-humidity map, an air processing simulation is performed using the humid air thermodynamic physical model to obtain comprehensive thermodynamic analysis results.

[0007] According to one embodiment of this application, when the humid air state parameters include dry-bulb temperature, the target evaluation strategy includes: directly calculating the moisture content based on the dry-bulb temperature and the first state parameter through the algebraic relationship in the humid air thermodynamic physical model, and solving for the humid air state parameter set based on the moisture content; the first state parameter is another parameter in the humid air state parameters.

[0008] According to one embodiment of this application, the humid air state parameters include a first type of parameter, which is a parameter for determining absolute humidity, including moisture content, dew point temperature, or water vapor partial pressure. When the humid air state parameters include the first type of parameter, the objective evaluation strategy includes: when the second state parameter is specific enthalpy or specific volume, performing an algebraic inverse solution based on the linear or quasi-linear relationship in the humid air thermodynamic physical model; wherein the second state parameter is another parameter among the humid air state parameters; when the second state parameter is wet-bulb temperature, constructing a monotonic objective function about dry-bulb temperature, and solving the monotonic objective function based on the bisection method; when the second state parameter is relative humidity, calculating the target saturated vapor pressure, and performing a numerical inverse solution of the saturated vapor pressure formula to obtain the dry-bulb temperature.

[0009] According to one embodiment of this application, the humid air state parameters include a second type of parameters, which are combined parameters obtained through a single main variable closed equation; when the humid air state parameters are second type of parameters, the objective evaluation strategy includes: constructing a residual function for the main variables in the second type of parameters, and solving the residual function based on a one-dimensional numerical iterative algorithm to obtain the set of humid air state parameters.

[0010] According to one embodiment of this application, the step of constructing a residual function for the main variables in the second type of parameters and solving the residual function based on a one-dimensional numerical iterative algorithm to obtain the set of humid air state parameters includes: when the second type of parameters is a combination of wet-bulb temperature and relative humidity, constructing a residual function based on the equality of water vapor partial pressure and performing iterative solution; when the second type of parameters is a combination of specific enthalpy and relative humidity, constructing a residual function based on the coupling of energy conservation and humidity definition and performing iterative solution; and when the second type of parameters is a combination of specific volume and relative humidity, constructing a residual function based on the coupling of state equation and humidity definition and performing iterative solution.

[0011] According to one embodiment of this application, the humid air state parameters include a third type of parameter, which is a combination parameter of strongly coupled nonlinear functions of dry-bulb temperature and moisture content; when the humid air state parameters are third type parameters, the objective evaluation strategy includes: constructing a nonlinear equation system containing at least two residual equations, and using a multidimensional numerical iterative algorithm based on the Jacobian matrix to jointly solve the nonlinear equation system to obtain the dry-bulb temperature and moisture content.

[0012] According to one embodiment of this application, the construction of a nonlinear equation system containing at least two residual equations, and the joint solution of the nonlinear equation system using a multidimensional numerical iterative algorithm based on the Jacobian matrix to obtain the dry-bulb temperature and moisture content, includes: when the third type of parameter is a combination of wet-bulb temperature and specific volume, constructing a nonlinear equation system based on the constraints of the dry-bulb and wet-bulb equations and the wet air state equation and solving it; when the third type of parameter is a combination of specific enthalpy and specific volume, constructing a nonlinear equation system based on the enthalpy definition equation and the wet air state equation and solving it.

[0013] According to one embodiment of this application, the step of mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map includes: defining the screen horizontal axis as corresponding to the moisture content, and the screen vertical axis as a specific linear combination of dry-bulb temperature and moisture content, so as to realize that the isoenthalpy lines are displayed as parallel lines at a specific angle in the enthalpy-humidity map.

[0014] According to one embodiment of this application, the method further includes: obtaining screen pixel coordinates in response to an interactive operation on the enthalpy-humidity diagram; obtaining the corresponding physical space state parameters by inverse transformation of the non-orthogonal linear transformation relationship; and triggering the invocation and calculation of the humid air thermodynamic physical model to update the humid air state parameter set and the comprehensive thermodynamic analysis results.

[0015] According to one embodiment of this application, the step of mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map further includes: during the generation of contour lines, detecting whether the current drawing point enters the saturation region; if it enters the saturation region, constructing a judgment function based on the saturation boundary, using a numerical iterative algorithm to search for a theoretical intersection point on the saturation curve, and using the theoretical intersection point as the drawing endpoint of the contour lines.

[0016] According to one embodiment of this application, the simulation of the air handling process based on the humid air thermodynamic physical model includes: receiving volumetric flow rate parameters of two airflows in response to an instruction for the air mixing process; calculating the specific volume based on the state parameters of each airflow and converting the volumetric flow rate into the dry air mass flow rate; calculating the mass flow rate ratio after mixing based on the dry air mass conservation law, and performing a weighted average of the enthalpy and moisture content to obtain the state parameters of the mixing point.

[0017] Secondly, this application provides a humid air thermodynamic analysis system based on coordinate mapping and multi-source iteration, comprising: a calculation module, used to receive a set of humid air state parameters, call a target evaluation strategy in a humid air thermodynamic physical model according to the combination type of the humid air state parameters, and iterate or directly calculate a set of humid air state parameters based on the target evaluation strategy; wherein, the target evaluation strategy is an evaluation strategy corresponding to the combination type of the humid air state parameters; a mapping module, used to map the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map; wherein, the enthalpy-humidity map includes state points and contour lines; and a simulation module, used to perform an air processing simulation based on the humid air thermodynamic physical model in the enthalpy-humidity map to obtain comprehensive thermodynamic analysis results.

[0018] Compared with existing technologies, the advantages of this application are as follows: It dynamically invokes the optimal objective evaluation strategy based on the type of input parameters, matching the most suitable and stable thermodynamic solution path for each parameter combination, thus reducing systemic biases caused by computational assumptions and approximations at the source. Cross-validation and convergence optimization of intermediate calculation results during the multi-source iterative process effectively suppresses the amplification of local errors. Maintaining the consistency of mathematical relationships in the transformation process from physical space to graphical pixel space based on non-orthogonal linear coordinate mapping avoids information distortion, ensuring strict numerical fidelity of state points and contour lines on the enthalpy-humidity diagram. Finally, process simulation based on highly accurate graphical representation improves the computational accuracy and engineering reliability of the comprehensive thermodynamic analysis results. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of the humid air thermodynamic analysis method based on coordinate mapping and multi-source iteration provided in the embodiments of this application.

[0020] Figure 2 The logical architecture diagram of the humid air thermal analysis system provided in the embodiments of this application is shown.

[0021] Figure 3 A flowchart illustrating the multi-branch iterative solution process of the humid air thermodynamic analysis method provided in this application embodiment.

[0022] Figure 4A schematic diagram illustrating the principle of non-orthogonal coordinate system mapping provided in the embodiments of this application.

[0023] Figure 5 This is a schematic diagram of the air mixing process calculation provided in an embodiment of this application.

[0024] Figure 6 A schematic diagram illustrating the principle of contour line generation and precise saturation boundary trimming provided in the embodiments of this application.

[0025] Figure 7 A flowchart illustrating comfort zone polygon boundary tracking and clipping provided in this application embodiment.

[0026] Figure 8 This is a schematic diagram of the main interface interaction of the system provided in an embodiment of this application. Detailed Implementation

[0027] The present application will now be described in further detail with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the subject matter of the present application to the following embodiments. All technologies implemented based on the content of the present application fall within the scope of protection of the present application.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" only distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] With the development of computer technology, various electronic humidity air calculation tools (such as EES, Excel plugins, or simple apps) are currently available on the market. However, when dealing with high-precision engineering problems, they generally suffer from the following technical shortcomings:

[0031] Firstly, most existing technologies only support standard forward calculations based on the "Gibbs phase law," meaning that either "dry-bulb temperature" plus either "relative humidity" or "wet-bulb temperature" must be input as known conditions. However, in practical engineering (such as coil performance analysis or sensor data inversion), non-standard parameter combinations are often encountered, for example: known specific enthalpy and specific volume (…). Existing tools typically cannot solve this directly because the functions of these two parameters with respect to temperature and humidity form a highly nonlinear transcendental system of equations, and no analytical solution exists. Given the wet-bulb temperature and specific volume (… Solving implicit equations involving wet-bulb temperature is extremely complex. Existing software typically lacks built-in, robust numerical iterative solvers (such as the multidimensional Newton-Raphson method), making such problems uncomputable or forcing engineers to use manual trial-and-error methods, which are inefficient and difficult to guarantee accuracy.

[0032] Secondly, existing tools are often based on standard atmospheric pressure-based algorithms. When dealing with varying altitudes, such as those at high altitudes, they typically only provide simple correction coefficients, failing to reconstruct the underlying formulas for water vapor partial pressure and saturation pressure. This leads to calculation errors in high-altitude regions increasing exponentially with rising temperature and humidity. When drawing contour lines (such as isotherms and isenthalpic lines), existing drawing engines often use simple polylines. When the lines cross saturation curves (…),… When entering a fog area, the lack of a physical boundary detection mechanism leads to drawing overflow or simple truncation, making it impossible to accurately calculate and draw the theoretical intersection of the line and the saturation curve, thus affecting the professionalism and readability of the graphics.

[0033] Thirdly, in the simulation of air handling processes, the mixing of air at different state points is a core element. Existing tools generally use a simplified "volume-weighted average method" to calculate the mixing point state. However, according to the ideal gas law... Air density varies significantly with different temperatures and humidity levels (e.g., cold air is much denser than hot air). Mixing calculations based solely on volumetric flow rate violate the law of conservation of mass. In scenarios involving the mixing of fresh and return air with large temperature differences, this simplified algorithm can lead to deviations in the calculation of the mixing point enthalpy, resulting in systematic errors in the calculation of cooling and heating loads and severely impacting the accuracy of system selection.

[0034] Fourthly, traditional software architectures typically separate the "computational kernel" from the "graphics display." Users can only generate static charts by inputting data and cannot reverse-engineer physical parameters in real time by dragging and dropping status points on the chart (inverse mapping is lacking). The oblique (non-orthogonal) coordinate system unique to enthalpy-humidity charts is difficult to accurately represent on the Cartesian coordinate system (orthogonal) of a computer screen. Existing software often uses background image overlay, which easily leads to misalignment between data points and the background grid when zoomed or panned, failing to meet the requirements for high-precision chart reading.

[0035] In summary, existing technologies urgently need an interactive analysis method that can integrate high-precision numerical solution algorithms, support rigorous physical process simulations (such as mass mixing and boundary clipping), and possess dynamic coordinate transformation capabilities. Based on this, this application provides a method for humid air thermodynamic analysis based on coordinate mapping and multi-source iteration. Please refer to [link to relevant documentation]. Figure 1, Figure 1 This diagram illustrates the steps of the moist air thermodynamic analysis method based on coordinate mapping and multi-source iteration provided in this application embodiment. The steps of the moist air thermodynamic analysis method based on coordinate mapping and multi-source iteration may include:

[0036] S1. Receive a set of moist air state parameters. Based on the combination type of the moist air state parameters, call the objective evaluation strategy in the moist air thermodynamic physical model. Obtain the set of moist air state parameters through iteration or direct calculation based on the objective evaluation strategy. The objective evaluation strategy is the evaluation strategy corresponding to the combination type of the moist air state parameters.

[0037] S2. Based on a non-orthogonal linear transformation relationship, the set of humid air state parameters is mapped from physical space to screen pixel space to generate an enthalpy-humidity map. The enthalpy-humidity map includes state points and contour lines.

[0038] S3. In the enthalpy-humidity diagram, the air handling process is simulated based on the thermodynamic physical model of humid air to obtain the comprehensive thermodynamic analysis results.

[0039] In this embodiment, moist air state parameters refer to physical quantities used to describe the thermodynamic state of moist air (a mixture of dry air and water vapor), which may include dry-bulb temperature, wet-bulb temperature, dew point temperature, relative humidity, moisture content (absolute humidity), specific enthalpy, specific volume (or density), and ambient pressure (or altitude), etc. These parameters are coupled based on thermodynamic laws; for example, knowing two independent parameters can, in principle, determine the remaining states of the air. The combination type of moist air state parameters characterizes the mathematical and physical relationships between the parameters in a set of moist air state parameters. In this embodiment, the combination type refers to a category classified according to whether the input parameters can be directly solved using algebraic formulas or require iterative solutions using single-variable / multi-variable implicit equations. For example, the combination of "dry-bulb temperature and relative humidity" usually belongs to the type that can be directly solved algebraically; while the combination of "specific enthalpy and specific volume" belongs to the strongly coupled type that requires multi-dimensional iterative solutions using a system of simultaneous nonlinear equations.

[0040] A moist air thermodynamic physical model is a set of mathematical equations and constants used to calculate the relationships between various state parameters of moist air. In the embodiments of this application, the model may include a saturated water vapor pressure calculation model based on the Hyland-Wexler or Goff-Gratch formula, a moisture content and partial pressure conversion relationship based on Dalton's law of partial pressures, a specific volume calculation model based on the ideal gas law, and a specific enthalpy calculation model based on energy conservation. The moist air thermodynamic physical model dynamically incorporates ambient pressure as a core variable in all calculations, rather than using a fixed value.

[0041] The construction process of the humid air thermodynamic physical model includes: establishing the basic assumption that humid air is an ideal gas mixture, and defining a set of core thermodynamic constants and equations of state as the unified physical basis for all system calculations and simulations. The model incorporates a series of fundamental thermodynamic constants, including the gas constants for dry air. Water vapor gas constant Specific heat capacity of dry air at constant pressure Specific heat capacity of water vapor at constant pressure and a reference latent heat of vaporization at one standard atmosphere. These constants form the basis of the coefficients for all derived calculation formulas.

[0042] For saturated water vapor pressure The calculations employ a piecewise high-precision fitting strategy to ensure accuracy across the entire temperature range. Specifically, the system will adjust the model based on the current dry-bulb temperature. Is it below freezing point? It automatically selects the appropriate saturated vapor pressure calculation formula for ice or liquid surfaces, such as the Hyland-Wexler equation or the Goff-Gratch equation.

[0043] For example, for the liquid surface ( The calculation formula is in the form of For ice surface ( ), then the form is The formula is used for calculation, where Absolute temperature to is the fitting constant.

[0044] Based on the above, the state parameters of the model are defined and the transition logic is as follows:

[0045] Moisture content With water vapor partial pressure Interchange: , ,in Total ambient pressure. Relative humidity. Definition: Specific enthalpy Calculation: Specific volume (or density) Calculation of ) .

[0046] This physical model incorporates environmental pressure. As a core variable, it is dynamically integrated into all calculation formulas, rather than being a fixed value, thus providing a fundamental guarantee for the system to achieve high-precision calculations under varying pressure environments.

[0047] The objective evaluation strategy is a numerical calculation path and algorithm that matches a specific combination of humid air state parameters. In the embodiments of this application, the objective evaluation strategy can be the calculation logic built into the humid air thermodynamic analysis system (hereinafter referred to as the system) for inversely calculating all unknown parameters from known parameters. It can include explicit algebraic operations, one-dimensional numerical iteration (such as Newton's method and bisection method), multi-dimensional coupled iteration (such as the Newton-Raphson method based on the Jacobian matrix), etc.

[0048] Please refer to Figure 2 , Figure 2 The logical architecture diagram of the humid air thermal analysis system provided in the embodiments of this application is shown. Figure 2 The document demonstrates the overall data flow and interactive feedback mechanism from parameter input and core physics engine calculations to the graphics rendering layer. The following section introduces the system implementing a coordinate mapping and multi-source iteration-based method for humid air thermodynamic analysis. The system adopts a layered architecture, comprising three tightly coupled subsystems: a multi-source parameter implicit equation numerical solution kernel, a process analysis model based on dry air mass conservation, and a non-orthogonal coordinate system dynamic mapping and precise clipping engine.

[0049] Specifically, to address the nonlinear coupling relationships between the state parameters of moist air, the computation module is a numerical solution kernel for a multi-source parameter implicit equation system, which may include a multidimensional Newton-Raphson iterative solver, and is designed for highly coupled non-standard parameter combinations (such as "known specific enthalpy"). and specific volume The solver constructs a system of nonlinear equations containing two residual equations:

[0050]

[0051] in, Let represent a vector function consisting of two residual equations. Indicates the dry-bulb temperature based on the current iteration estimate. and moisture content , Given the specific enthalpy value, Indicates the estimate based on the current iteration value , and given pressure , Given the specific volume value, the Jacobian matrix and its determinant are calculated in real time using analytical methods. Step-size iterations are performed by matrix inversion, thus simultaneously converging to obtain the dry-bulb temperature and moisture content.

[0052] Furthermore, to address problems of varying complexity and ensure robustness, this numerical solution kernel employs a highly robust hybrid iterative strategy. For univariate implicit problems (such as "given wet-bulb temperature"), this strategy is particularly effective. and relative humidity The system prioritizes the one-dimensional Newton method, which has a fast convergence speed, for solving the problem. When the derivative is detected to be close to zero or the iteration may diverge, the system will automatically and seamlessly revert to the bisection approximation method, which has an absolute convergence guarantee, thereby maintaining the stability of the calculation under extreme or sensitive conditions.

[0053] Another core feature of this solver kernel is the reconstruction and dynamic integration of the underlying physical model. The system incorporates high-precision saturated vapor pressure calculation models such as the Hyland-Wexler formula and integrates dynamic atmospheric pressure... The system is directly incorporated as a global variable into all core state equations (such as the formulas for calculating moisture content, enthalpy, and specific volume) for solution, rather than simply correcting the results calculated based on standard atmospheric pressure. This reconstruction at the underlying formula level fundamentally ensures the system's calculation accuracy under non-standard atmospheric pressure (such as in high-altitude areas) and overcomes the error amplification problem caused by simplification corrections in traditional methods.

[0054] Please refer to Figure 3 , Figure 3 A flowchart illustrating the multi-branch iterative solution process of the humid air thermodynamic analysis method provided in this application embodiment. Figure 3 This demonstrates algebraic analysis, bisection method, or Newton's iteration solution strategies employed for different combinations of known parameters (explicit / semi-implicit / purely implicit). The multi-branch iterative solution process is detailed below:

[0055] When the dry-bulb temperature is included in the state parameters of moist air, the objective evaluation strategy includes: directly calculating the moisture content based on the dry-bulb temperature and the first state parameter through the algebraic relationship in the thermodynamic physical model of moist air, and solving for the set of state parameters of moist air based on the moisture content; the first state parameter is another parameter in the state parameters of moist air.

[0056] For example, when the user input includes dry-bulb temperature, a parameter that can be directly used as the independent variable, the system can determine that the problem belongs to the explicit algebraic solution type, and thus call a direct calculation path that does not require iteration. The first state parameter is usually another parameter that, together with the dry-bulb temperature, constitutes an independent state description; for example, the dry-bulb temperature is known. and relative humidity The calculation path is as follows: 1. Directly substitute into the saturation pressure formula for calculation. 2. According to the definition 3. Calculate the partial pressure of water vapor; 4. Derive the moisture content using Dalton's law of partial pressures. 4. Based on and Calculate enthalpy Specific volume And other parameters.

[0057] Optionally, the humid air state parameters include first-type parameters, which are parameters for determining absolute humidity, and may include moisture content. Dew point temperature Or water vapor partial pressure When the moist air state parameters include first-type parameters, the objective evaluation strategy may include:

[0058] When the second state parameter is specific enthalpy or specific volume, an algebraic inverse solution is performed based on the linear or quasi-linear relationship in the thermodynamic physical model of moist air; where the second state parameter is another parameter in the state parameters of moist air.

[0059] In the case where the first type of parameter is moisture content W and the second state parameter is specific enthalpy h, the algebraic inverse solution includes: based on the definition of specific enthalpy. This can be rearranged into a linear equation in one variable concerning the dry-bulb temperature t. Thus, t can be directly calculated, completing the solution for the state point.

[0060] With the second state parameter being wet-bulb temperature, a monotonic objective function for dry-bulb temperature is constructed, and the monotonic objective function is solved using the bisection method.

[0061] In this context, the first type of parameter is the moisture content W, and the second state parameter is the wet-bulb temperature. In this case, construct the objective function ,in This is a function for calculating wet-bulb temperature based on a physical model. Since this function is monotonic within the physical interval, the system operates within a reasonable temperature range (with the lower bound as...). The upper boundary is Apply a binary search iterative search within the range until... This allows for the determination of the dry-bulb temperature t with satisfactory accuracy. Although the convergence rate is linear, this method exhibits absolute convergence stability within the humid air physics range, avoiding the oscillation problem that may occur during fixed-point iteration.

[0062] With the second state parameter being relative humidity, the target saturated vapor pressure is calculated, and the dry-bulb temperature is obtained by numerically solving the saturated vapor pressure formula.

[0063] In this context, the first type of parameter is the moisture content W, and the second state parameter is the relative humidity. In this case, the strategy includes: first, calculating the water vapor partial pressure based on the moisture content W and the ambient pressure P. Then calculate the target saturated vapor pressure. ; then, As known values, the higher-order saturated vapor pressure formula in the physical model... By applying Newton's iteration method, the corresponding dry-bulb temperature t is numerically solved.

[0064] Optionally, the moist air state parameters include second-type parameters, which are combined parameters obtained through a single main variable closed equation;

[0065] When the moist air state parameters are of the second type, the objective evaluation strategy can include:

[0066] A residual function for the main variables in the second type of parameters is constructed, and the residual function is solved based on a one-dimensional numerical iterative algorithm to obtain the set of moist air state parameters.

[0067] In the case where the second type of parameter is a combination of wet-bulb temperature and relative humidity, an iterative solution is performed using a residual function based on the equality of water vapor partial pressure.

[0068] The second type of parameter is the known wet-bulb temperature. With relative humidity When combining these variables, the main variable is the dry-bulb temperature. Constructing the residual function involves: based on the dry-bulb temperature of the current iteration. and known The first theoretical water vapor partial pressure is calculated using the saturated vapor pressure formula and the definition of relative humidity. Meanwhile, based on the current iteration and known The second theoretical water vapor partial pressure is calculated using wet-bulb equations (such as the adiabatic saturation model). Constructing the residual function The solution involves applying the one-dimensional Newton-Raphson method through iterative formulas. Rapid convergence to the dry-bulb temperature at which the residuals are zero Then, the remaining parameters are calculated.

[0069] When the second type of parameter is a combination of specific enthalpy and relative humidity, an iterative solution is performed by constructing a residual function based on the coupling of energy conservation and humidity definition.

[0070] Among them, the second type of parameter is a known specific enthalpy. With relative humidity When combining these variables, the main variable is the dry-bulb temperature. Constructing the residual function involves: for the current iteration temperature Combined with known Environmental pressure Derivation of process moisture content ;Will and Substitute into the definition of specific enthalpy to calculate the theoretical enthalpy value. Constructing the residual function The solution involves using the one-dimensional Newton-Raphson iteration method, with continuous adjustments. The value is used to minimize the residual. Thus, a unique state point is locked under the conditions of energy conservation and humidity constraint.

[0071] When the second type of parameter is a combination of specific volume and relative humidity, an iterative solution is performed using a residual function that couples the state equation with the humidity definition.

[0072] Among them, the second type of parameter is a known specific volume. With relative humidity When combining these variables, the main variable is the dry-bulb temperature. Constructing the residual function involves: for the current iteration temperature According to known Export the corresponding moisture content ;Will , and environmental pressure Substitute into the equation of state for moist air and calculate the theoretical specific volume. Constructing the residual function The solution involves using one-dimensional numerical iteration (such as Newton's method) to solve the equation and obtain the dry-bulb temperature that satisfies the given volume and humidity conditions. This completes the inversion of the state point.

[0073] Optionally, the moist air state parameters include third-type parameters, which are combined parameters of strongly coupled nonlinear functions of dry-bulb temperature and moisture content; when the moist air state parameters are third-type parameters, the objective evaluation strategy may include:

[0074] A system of nonlinear equations containing at least two residual equations is constructed, and a multidimensional numerical iterative algorithm based on the Jacobian matrix is ​​used to solve the system of nonlinear equations jointly to obtain the dry-bulb temperature and moisture content.

[0075] When the third type of parameter is a combination of wet-bulb temperature and specific volume, a set of nonlinear equations is constructed and solved based on the constraints of the wet-bulb equation and the wet air state equation.

[0076] Among them, the third type of parameter is wet-bulb temperature. with specific volume In the case of a combination of dry-bulb and wet-bulb equations and wet air state equations, a set of nonlinear equations is constructed and solved.

[0077] Input-based and what is pending The theoretical water vapor partial pressure was calculated using a high-precision hygrometer formula (the psychrometer equation considering the effect of wind speed). ; at the same time based on the demand Calculate the theoretical partial pressure The two must be equal.

[0078] Based on the demand and The theoretical specific volume is calculated using the wet air state equation and then made equal to the input value. .

[0079] Calculate the values ​​of the two equations above. and Using the partial derivatives, construct the Jacobian matrix and iteratively update synchronously. and This can solve the problem that traditional algorithms are prone to divergence under this combination.

[0080] When the third type of parameter is a combination of specific enthalpy and specific volume, a set of nonlinear equations is constructed based on the definition of enthalpy and the equation of state of moist air for solution.

[0081] Among them, the third type of parameter is enthalpy. with specific volume In the case of a combination of these equations, a system of nonlinear equations is constructed based on the enthalpy definition equation and the wet air state equation for solution.

[0082] The nonlinear equation system is constructed as follows:

[0083]

[0084] By calculating the Jacobian matrix:

[0085]

[0086] Calculate the step size using matrix inversion: Synchronous updates are achieved using the Newton-Raphson iterative method. and This effectively solves the technical problem of back-calculating the air state point when only the enthalpy value and air volume (volume flow) of the coil outlet are known.

[0087] The system's mapping module employs a non-orthogonal coordinate system dynamic mapping and precise clipping engine. This engine is used to accurately draw oblique axis enthalpy-humidity maps on orthogonal computer screens and achieve high-fidelity interaction. The engine achieves dynamic coordinate mapping through non-orthogonal linear transformations. Please refer to [link to relevant documentation]. Figure 4 , Figure 4 The diagram below illustrates the principle of non-orthogonal coordinate system mapping in an embodiment of this application, showing the forward transformation and reverse picking logic between thermodynamic physical state points and screen pixel coordinates.

[0088] Because the isenthalpic lines on a standard enthalpy-humidity map are slanted lines, while the computer screen uses a Cartesian orthogonal coordinate system, the engine constructs a linear transformation matrix. This matrix maps the physical state point from a physical space defined by dry-bulb temperature and moisture content to screen pixel coordinates (X, Y). Here, the ordinate Y does not directly correspond to temperature, but is defined as a specific linear combination of t and W related to enthalpy (e.g., ...). (One implementation). This transformation maps isenthalpic lines in physical space to a set of parallel straight lines in screen space, thus visually perfectly reproducing the oblique axis geometry unique to engineering enthalpy-humidity charts. When users zoom or pan the chart, the engine only needs to perform affine operations on this transformation matrix to efficiently update the entire view, ensuring smooth graphical operations.

[0089] The engine integrates precise saturation boundary clipping logic. When drawing any contour lines, such as isenthalpic lines and isotherms, it employs a physical intersection strategy: when a point on the contour line is detected to exceed the saturation boundary in real time, regular drawing is immediately paused, and an objective function is constructed based on the contour line type (e.g., isenthalpic or isodense). A theoretical intersection point is then actively searched on the saturation curve using numerical methods (e.g., bisection). This precisely calculated intersection point is then used as the endpoint for drawing. This ensures that all contour lines terminate smoothly and accurately at the saturation curve.

[0090] The engine also features inverse interactive picking functionality. By monitoring the pixel coordinates of the mouse within the drawing area, the engine uses the inverse of the aforementioned non-orthogonal linear transformation to continuously and in real-time convert the screen coordinates into the corresponding physical parameters: dry-bulb temperature (t) and moisture content (W). After acquiring these two key parameters, the system automatically triggers the calculation kernel, instantly updating and displaying the complete set of thermodynamic state parameters for that point. This allows users to intuitively explore parameter changes and verify design schemes by directly dragging and dropping state points on the chart, achieving true "what you see is what you get" interactive analysis.

[0091] Please refer to Figure 5 , Figure 5 This diagram illustrates the calculation of the air mixing process provided in this embodiment. The system's simulation module employs a process analysis model based on the conservation of dry air mass. This model, by adhering to the law of conservation of mass, fundamentally corrects the physical deviations present in the traditional "volume mixing method" for simulating air handling processes. When handling the crucial process of mixing two airflows, the model first calculates their specific volume based on their respective state parameters. and Then the input volumetric flow rate will be... Real-time conversion to dry air mass flow rate This step, through density correction, can objectively reflect the density differences of air at different temperatures and humidity levels. The state calculation of the mixing point is no longer based on the volume ratio, but on the dry air mass flow rate ratio derived from it. Regarding enthalpy value and moisture content A weighted average is used to accurately solve for the mixed state point. All parameters.

[0092] In terms of process analysis, the model abstracts any air handling process as a vector movement of state points on the enthalpy-humidity diagram and automatically calculates the heat-to-moisture ratio of the process vector to determine the nature of the process (such as heating and humidification, cooling and dehumidification, etc.). Combined with the input air volume (converted to mass flow rate), the model can decompose and calculate the total heat load, sensible heat load, latent heat load, and moisture load corresponding to the process in real time, providing direct data support for equipment selection and energy efficiency analysis. Simultaneously, the model incorporates physical boundary monitoring logic, which can determine in real time whether the process line crosses the saturation curve (causing condensation) and automatically trigger calculations for complex paths such as dehumidification and reheat, making the simulation more closely resemble engineering practice.

[0093] At the visualization level, the model works closely with the graphics rendering engine. The simulation-generated sequence of state points, process vectors, and mixing point locations are mapped onto the enthalpy-humidity map in real time, dynamically plotted as directional trajectory lines. The mixing points are accurately located on the connecting lines according to the actual mass mixing ratio, intuitively demonstrating their physical meaning.

[0094] In some embodiments, mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship can include two steps: forward mapping and reverse mapping, and integrate precise cropping and interactive picking functions.

[0095] Forward mapping converts physical state parameters into screen coordinates for drawing. The system defines the screen horizontal coordinate. With moisture content Proportional (usually after unit conversion, for example) (Convert kg / kg to g / kg). Screen vertical axis Then it is defined by a specific linear combination, for example ,in Dry bulb temperature, and These are the isobaric specific heat capacities of dry air and water vapor, respectively. The physical essence of this transformation lies in the fact that this linear combination is approximately equivalent to a linearization of the enthalpy formula for moist air, achieved through isenthalpy lines that are curves in physical space (…). In this transformed screen coordinate system, the graph is mapped to a series of parallel straight lines with a fixed slope (e.g., 135 degrees), thus visually accurately reproducing the core features of the engineering enthalpy-humidity diagram. When zooming or panning the view, the system only needs to perform an efficient affine transformation on this mapping relationship to update the entire graphic display, without having to perform complex thermodynamic calculations again.

[0096] Then, reverse mapping and graphic clipping are performed to achieve high-precision graphic generation. When the system generates isenthalpic lines, isotherms, and other primitives based on physical parameters, it will detect in real time whether each discrete point to be drawn has entered the "fog zone" (i.e., relative humidity). Once an out-of-bounds error is detected, precise clipping of the saturation boundary is immediately initiated. See also... Figure 6 , Figure 6 A schematic diagram illustrating the principle of contour line generation and precise saturation boundary trimming provided in the embodiments of this application.

[0097] The system does not simply truncate the lines, but rather constructs a boundary judgment function with temperature as the variable based on the physical definition of the contour lines (such as isenthalpy or isodensity). And using numerical methods (such as the bisection method) on the saturation curve ( The algorithm actively searches for theoretical intersection points and uses these calculated precise intersection points as the endpoints for drawing the lines. This ensures that all contour lines terminate smoothly and accurately at saturation boundaries, maintaining physical rigor in the graphics at any scaling level.

[0098] The point set that generates any contour lines During the process, the system calculates each discrete point Perform real-time saturation detection: Once detected (i.e., upon entering the fog zone), immediately stop generating subsequent points and start the boundary intersection subroutine.

[0099] For different types of physical contour lines, the system can employ different mathematical strategies to calculate their correlation with the saturation curve. The precise intersection of ) .

[0100] In the first strategy, for isotherms, an explicit equation intersection method can be used. For isotherms ( The saturation intersection point is the saturation point at that temperature. Direct calculation... And directly calculate saturated moisture content Thus, the coordinates of the intersection point are obtained: .

[0101] In the second strategy, for isenthalpic lines and isodense lines, an implicit equation numerical intersection method can be used. For isenthalpic lines ( The temperature at the intersection of its saturation line. Unknown, and satisfies nonlinear equations The system can be solved using the binary approximation method:

[0102] The constructed objective function is .in, It's about temperature. Complex functions (involving higher-order saturation pressure formulas). Setting the temperature search range. (For example to Iterative solution: Calculate the midpoint. ;like The root is in the left half of the interval, let Otherwise, Repeat until the interval width is reached. (like Determine the intersection point: the point that eventually converges. That is, the intersection temperature, combined with Obtain the precise intersection point. Use the calculated precise intersection point... The points are appended to the end of the point set as the endpoint, and then the point set is sent to the graphics rendering engine. This mechanism ensures that all contour lines terminate "perfectly" at the saturation curve, and are visually flawless no matter how much they are magnified.

[0103] In addition, the system incorporates a thermal comfort zone generation engine compliant with the ASHRAE 55 standard. Unlike traditional software that only supports rectangular areas based on temperature and relative humidity, this invention implements a dynamic polygon generation algorithm capable of handling upper limits on moisture content (absolute humidity). Please refer to [link / reference]. Figure 7 , Figure 7 A flowchart illustrating comfort zone polygon boundary tracking and clipping provided in this application embodiment. The dynamic polygon generation algorithm may include:

[0104] 1. Multidimensional parameter constraint definition: Users can interactively set three sets of physical constraint boundaries: dry bulb temperature range: Relative humidity range: Moisture content range: (New constraints added to limit the risk of mold growth).

[0105] 2. Boundary tracking and dynamic cropping algorithm: Since the moisture content isopleths are horizontal on the enthalpy-humidity map, while the relative humidity line is curved, the intersection of these three is often an irregular polygon. The system uses a "boundary walking" state machine logic to generate accurate closed shapes.

[0106] Initialization starts from the bottom left corner. Begin. Scan along the four basic boundary paths in sequence: along Isohypermeability line, temperature from Increase to ;along Isotherms, humidity from Increase to ;along Isohypermeability line, temperature from Down to ;along Isotherms, humidity from Down to .

[0107] When generating boundary points along the aforementioned path, the system continuously monitors whether the moisture content of the current point exceeds the limit. Range. Once a boundary crossing is detected, the core solver is immediately invoked to calculate the precise intersection of the current path (such as an isotherm) and the moisture content limit line (isohygrometer), and this intersection is used as the inflection point of the polygon.

[0108] 3. Graphic closure and rendering: The algorithm ultimately outputs an ordered set of points that has undergone logical clipping. The system maps these physical coordinates to screen coordinates using the aforementioned non-orthogonal transformation matrix, and uses the graphics engine to draw closed polygons with semi-transparent fill colors, thus visually presenting irregular effective comfort areas on the chart.

[0109] In some embodiments, the method provided in this application may further include:

[0110] In response to interactive operations on the enthalpy-humidity diagram, the screen pixel coordinates are obtained; the corresponding physical space state parameters are obtained by inverse transformation of the non-orthogonal linear transformation relationship; the humid air thermodynamic physical model is invoked and calculated to update the humid air state parameter set and comprehensive thermodynamic analysis results.

[0111] The system can continuously monitor user interaction events (such as mouse movement or clicks) within the enthalpy-humidity chart plotting area. It captures screen pixel coordinates and converts them into chart data coordinates. Then, by applying the inverse transformation of the above-mentioned forward mapping relationship, the corresponding physical parameter—moisture content—is solved in real time. and dry bulb temperature (pass (The inverse operation formula is used to obtain the result). Subsequently, the system immediately outputs the solution obtained from the inverse operation. As new known conditions, the core solver is automatically triggered to recalculate and update the complete set of moist air state parameters, which are then displayed in real time on the interface. This mapping mechanism completely decouples the physics calculation kernel from the graphics rendering layer, allowing operations such as scaling and panning of the charts to be performed only on the physical calculation kernel and the graphics rendering layer. It performs simple matrix affine transformations without repeating complex thermodynamic iterative calculations, greatly improving rendering efficiency.

[0112] In some embodiments, the system abstracts the air handling process as a vector movement of state points on an enthalpy-humidity diagram. To address the errors caused by neglecting changes in air density in traditional calculations, this application implements a process simulation algorithm that follows the law of conservation of mass. The implementation of the process simulation algorithm encompasses three closely interconnected levels: physical process calculation, thermodynamic analysis, and graphical rendering.

[0113] The computational basis of the process simulation is a physical model that strictly adheres to the law of conservation of dry air mass. The system abstracts processes such as air mixing, heating, and cooling as vector movements of state points on the enthalpy-humidity diagram and performs calculations based on the reconstructed physical model. Taking the most typical two-air mixing process as an example, traditional methods using volume-weighted averaging introduce significant errors. The system, however, performs density correction. Based on the state points (A and B) of the two air streams, the system calculates their specific volume using the physical model. and When the user inputs volumetric flow rate At that time, the system first converts it into dry air mass flow rate. Subsequently, based on the dry air mass flow rate ratio Regarding enthalpy value and moisture content Perform weighted average This allows the core solver to accurately solve for all state parameters of the mixed state M of states A and B. Additionally, the system also supports a fast mixing mode based on the input volume ratio; for example, when the user only inputs the mixing volume ratio (e.g., the proportion of A). When this is done, the system first performs dimensionless density correction: Constructing the correction factor: and Derivation of the equivalent mass ratio: Then, step D above is reused for state synthesis. Essentially, this involves constructing a correction factor related to specific volume to equate the volume ratio to the mass ratio, thus ensuring physical consistency under different input methods.

[0114] For a process that moves from one state point A to another state point B (such as the cooling and dehumidification of air by an air conditioning coil), the system defines it as a process vector. And it can automatically perform the following parsing:

[0115] 1. Calculate the heat-to-moisture ratio: The system calculates the heat-to-moisture ratio according to the formula. The slope of the process line (heat-to-moisture ratio) is calculated to visually determine the nature of the process (such as heating, humidification, dehumidification, etc.).

[0116] 2. Decompose and calculate energy load: Combine the input air volume (the system automatically converts it into mass flow rate). Based on the laws of conservation of energy and mass, the system decomposes and calculates the total heat load, sensible heat load, latent heat load, and wet load required for the process. The specific formula is: Total Heat Load Latent heat load Sensible heat load These calculation results constitute the key quantitative data portion of the "Comprehensive Thermodynamic Analysis Results".

[0117] Finally, the results of all physical calculations are mapped onto an enthalpy-humidity chart in real time, forming a dynamic and intuitive visualization. The system maintains an ordered set of process points and uses a graphics engine to achieve the following visualization rendering:

[0118] The state points involved in the process (such as points A and B before mixing, point M after mixing, and the start and end points of the processing) are converted into screen coordinates through coordinate mapping and connected by line segments with directional arrows to clearly show the path of state changes.

[0119] During the mixing process, the system will adjust the mixing ratio based on the calculated mass ratio. The system accurately marks the location of the mixing point M (closer to the side with the larger mass flow rate) on the line segment connecting points A and B. Simultaneously, the system automatically matches different line colors and styles based on the process type (e.g., red for heating, blue for cooling), greatly enhancing the readability of the engineering drawings.

[0120] In the above implementation process, the optimal objective evaluation strategy is dynamically invoked based on the type of input parameters, matching the most suitable and stable thermodynamic solution path for each parameter combination, thereby reducing systematic biases caused by computational assumptions and approximations from the source. Cross-validation and convergence optimization of intermediate calculation results during the multi-source iterative process effectively suppresses the amplification of local errors. Maintaining the consistency of mathematical relationships during the transformation from physical space to graphical pixel space based on non-orthogonal linear coordinate mapping avoids information distortion, ensuring strict numerical fidelity of state points and contour lines on the enthalpy-humidity diagram. Finally, process simulation based on highly accurate graphical representation improves the computational accuracy and engineering reliability of the comprehensive thermodynamic analysis results.

[0121] In some other embodiments, although the Newton-Raphson method and bisection approximation method are mainly used for implicit equations in the above description, the core lies in the solution strategy of constructing the residual equation and iteratively converging. Therefore, the following algorithms can also be used as equivalent alternatives, such as the secant method or Brent's method, to replace the one-dimensional Newton method for single variables, achieving fast convergence without explicitly calculating derivatives. Multidimensional optimization algorithms are aimed at... For multivariate coupled problems, in addition to using the Jacobian matrix to solve the linear equation system, a least-squares objective function can also be constructed. The global optimum can be found using the Gauss-Newton method, the Levenberg-Marquardt algorithm, or the particle swarm optimization algorithm.

[0122] The system provided in this embodiment incorporates the Hyland-Wexler saturated vapor pressure equation and the ASHRAE fundamental equation of state. Without altering the system architecture, the physical kernel can be replaced with: the IAPWS-IF97 / IAPWS-95 standard for higher-precision industrial-grade steam calculations; the Goff-Gratch formula or the Magnus approximation formula for embedded scenarios requiring extremely high computational speed but slightly lower accuracy; and the real gas equation of state, which can introduce compressibility factors in high-pressure chemical applications. Replace the ideal gas assumption with the van der Waals equation or the Peng-Robinson EOS.

[0123] Optionally, the system provided in this application can directly interface with the data streams from temperature and humidity sensors and flow meters at the building site, and plot the dynamic operating trajectory on the enthalpy-humidity chart in real time for air conditioning unit fault diagnosis and energy efficiency assessment. It can also integrate a typical meteorological year database to calculate the hourly air handling load for the whole year through batch processing mode, and generate an annual operating energy consumption distribution map.

[0124] Optionally, the simulation of the air handling process is performed based on the thermodynamic physical model of the humid air. The step of mapping the set of humid air state parameters from the physical space to the screen pixel space based on the non-orthogonal linear transformation relationship to generate an enthalpy-humidity map further includes: during the generation of contour lines, detecting whether the current drawing point has entered the saturation region; if it has entered the saturation region, constructing a judgment function based on the saturation boundary, using a numerical iterative algorithm to search for the theoretical intersection point on the saturation curve, and using the theoretical intersection point as the drawing endpoint of the contour lines.

[0125] In this embodiment of the application, when drawing contour lines (such as isenthalpic lines) that cross the saturation region, it is not a simple graphical truncation, but rather the construction of an objective function and the use of bisection / numerical methods to search for its theoretical intersection with the saturation curve, thereby solving the problem of graphical distortion or overflow.

[0126] Optionally, the simulation of the air handling process based on the humid air thermodynamic physical model includes: receiving the volumetric flow rate parameters of the two airflows in response to the command of the air mixing process; calculating the specific volume according to the state parameters of each airflow and converting the volumetric flow rate into the dry air mass flow rate; calculating the mass flow rate ratio after mixing based on the dry air mass conservation law, and performing a weighted average of the enthalpy and moisture content to obtain the state parameters of the mixing point.

[0127] Based on the same concept, the wet air thermodynamic analysis system based on coordinate mapping and multi-source iteration provided in the embodiments of this application may include:

[0128] The calculation module receives a set of moist air state parameters, calls the target evaluation strategy in the moist air thermodynamic physical model according to the combination type of the moist air state parameters, and obtains the set of moist air state parameters by iterating or directly calculating based on the target evaluation strategy; wherein, the target evaluation strategy is the evaluation strategy corresponding to the combination type of the moist air state parameters.

[0129] The mapping module is used to map the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship, generating an enthalpy-humidity map; wherein, the enthalpy-humidity map includes state points and contour lines;

[0130] The simulation module is used to simulate the air handling process based on the thermodynamic physical model of humid air in the enthalpy-humidity diagram, and obtain comprehensive thermodynamic analysis results.

[0131] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the main interface of the system provided in this application embodiment. In the main interface, the parameters of the air state point calculation panel on the left (such as dry bulb temperature and relative humidity) are set, and the real-time rendering effect of the air state points and labels, process processing lines and multi-constraint thermal comfort zone (polygon fill) in the enthalpy-humidity diagram drawing area on the right is displayed.

[0132] Although the solutions provided in this application are desktop applications based on the .NET / WPF framework, their core logic and interface rendering are highly decoupled, making them easy to migrate to other platforms, such as cloud-based SaaS services. The core computing engine is encapsulated as a RESTful API or gRPC microservice and deployed on a cloud server. User terminals (such as web browsers or WeChat mini-programs) are only responsible for sending input parameters and receiving calculation results or SVG / Canvas drawing instructions. Embedded edge computing: A streamlined C# or C++ computing kernel is ported to the DDC controller of a building automation system (BAS) or a handheld industrial instrument to achieve real-time conversion of field data and control logic judgment. WebGL / 3D rendering extension: The graphics engine can be extended from two dimensions to Three.js or Unity to construct a three-dimensional humid air state space, intuitively displaying the three-dimensional change trajectory of multiple parameters (such as temperature, humidity, and pressure).

[0133] It should be understood that when the various modules of the system provided in the above embodiments are working, the division of each functional module in the above description is only used as an example. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0134] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0135] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for thermodynamic analysis of moist air based on coordinate mapping and multi-source iteration, characterized in that, include: A set of humid air state parameters is received, and a target evaluation strategy in the humid air thermodynamic physical model is invoked according to the combination type of the humid air state parameters. The humid air state parameter set is obtained by iterative calculation or direct calculation based on the target evaluation strategy; wherein, the target evaluation strategy is the evaluation strategy corresponding to the combination type of the humid air state parameters. The set of humid air state parameters is mapped from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map; wherein, the enthalpy-humidity map includes state points and contour lines; Based on the enthalpy-humidity diagram, the air handling process is simulated using the humid air thermodynamic physical model to obtain comprehensive thermodynamic analysis results. When the dry-bulb temperature is included in the humid air state parameters, the objective evaluation strategy includes: Based on the dry-bulb temperature and the first state parameter, the moisture content is directly calculated using the algebraic relationship in the thermodynamic physical model of the moist air, and the set of moist air state parameters is obtained by solving based on the moisture content; the first state parameter is another parameter in the set of moist air state parameters. The humid air state parameters include a first type of parameter, which is a parameter for determining absolute humidity. This first type of parameter includes moisture content, dew point temperature, or water vapor partial pressure. When the humid air state parameters include the first type of parameter, the objective evaluation strategy includes: When the second state parameter is specific enthalpy or specific volume, an algebraic inverse solution is performed based on the linear or quasi-linear relationship in the humid air thermodynamic physical model; wherein, the second state parameter is another parameter among the humid air state parameters; when the second state parameter is wet-bulb temperature, a monotonic objective function about dry-bulb temperature is constructed, and the monotonic objective function is solved based on the bisection method; when the second state parameter is relative humidity, the target saturated vapor pressure is calculated, and the saturated vapor pressure formula is numerically inversely solved to obtain the dry-bulb temperature; The moist air state parameters include second-type parameters, which are combined parameters obtained through a single main variable closed equation; when the moist air state parameters are second-type parameters, the objective evaluation strategy includes: The process involves constructing residual functions for the main variables in the second type of parameters and solving these residual functions using a one-dimensional numerical iterative algorithm to obtain the set of humid air state parameters. This includes: when the second type of parameters are a combination of wet-bulb temperature and relative humidity, constructing a residual function based on the equality of water vapor partial pressure and solving iteratively; when the second type of parameters are a combination of specific enthalpy and relative humidity, constructing a residual function based on the coupling of energy conservation and humidity definition and solving iteratively; and when the second type of parameters are a combination of specific volume and relative humidity, constructing a residual function based on the coupling of the state equation and humidity definition and solving iteratively. The moist air state parameters include third-type parameters, which are combined parameters of strongly coupled nonlinear functions of dry-bulb temperature and moisture content; when the moist air state parameters are third-type parameters, the objective evaluation strategy includes: A system of nonlinear equations containing at least two residual equations is constructed, and a multidimensional numerical iterative algorithm based on the Jacobian matrix is ​​used to jointly solve the system of nonlinear equations to obtain the dry-bulb temperature and moisture content. This process includes: when the third type of parameter is a combination of wet-bulb temperature and specific volume, constructing and solving a system of nonlinear equations based on the constraints of the dry-bulb and wet-bulb equations and the moist air state equation; and when the third type of parameter is a combination of specific enthalpy and specific volume, constructing and solving a system of nonlinear equations based on the enthalpy definition equation and the moist air state equation.

2. The method of claim 1, wherein, The process of mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map includes: Define the horizontal axis of the screen to correspond to the moisture content, and the vertical axis of the screen to be a specific linear combination of dry-bulb temperature and moisture content, so as to display the isenthalpic lines as parallel lines at a specific angle on the enthalpy-humidity diagram.

3. The method of claim 1, wherein, The method further includes: In response to an interactive operation on the enthalpy-humidity map, obtain the screen pixel coordinates; The corresponding physical space state parameters are obtained by inverse transformation of the non-orthogonal linear transformation relationship. The call and calculation of the humid air thermodynamic physical model are triggered to update the set of humid air state parameters and the comprehensive thermodynamic analysis results.

4. The method of claim 1, wherein, The step of mapping the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship to generate an enthalpy-humidity map further includes: During the generation of contour lines, it is checked whether the current drawing point has entered the saturation region. If it has entered the saturation region, a judgment function based on the saturation boundary is constructed, and a numerical iterative algorithm is used to search for the theoretical intersection point on the saturation curve. The theoretical intersection point is then used as the drawing endpoint of the contour line.

5. The method of claim 1, wherein, The simulation of the air handling process based on the aforementioned thermodynamic physical model of moist air includes: In response to instructions regarding the air mixing process, it receives the volumetric flow rate parameters of the two airflows; Calculate the specific volume based on the state parameters of each airflow, and convert the volumetric flow rate into the dry air mass flow rate; The mass flow rate ratio after mixing is calculated based on the law of conservation of dry air mass, and the state parameters at the mixing point are obtained by weighted averaging of enthalpy and moisture content.

6. A system for thermodynamic analysis of moist air based on coordinate mapping and multi-source iteration, characterized in that, include: The calculation module is used to receive a set of moist air state parameters, call the target evaluation strategy in the moist air thermodynamic physical model according to the combination type of the moist air state parameters, and obtain the set of moist air state parameters by iterative calculation or direct calculation based on the target evaluation strategy; wherein, the target evaluation strategy is the evaluation strategy corresponding to the combination type of the moist air state parameters; The mapping module is used to map the set of humid air state parameters from physical space to screen pixel space based on a non-orthogonal linear transformation relationship, thereby generating an enthalpy-humidity map; wherein the enthalpy-humidity map includes state points and contour lines; The simulation module is used to perform a simulation of the air handling process based on the thermodynamic physical model of the moist air in the enthalpy-humidity diagram, and obtain comprehensive thermodynamic analysis results. When the dry-bulb temperature is included in the humid air state parameters, the objective evaluation strategy includes: Based on the dry-bulb temperature and the first state parameter, the moisture content is directly calculated using the algebraic relationship in the thermodynamic physical model of the moist air, and the set of moist air state parameters is obtained by solving based on the moisture content; the first state parameter is another parameter in the set of moist air state parameters. The humid air state parameters include a first type of parameter, which is a parameter for determining absolute humidity. This first type of parameter includes moisture content, dew point temperature, or water vapor partial pressure. When the humid air state parameters include the first type of parameter, the objective evaluation strategy includes: When the second state parameter is specific enthalpy or specific volume, an algebraic inverse solution is performed based on the linear or quasi-linear relationship in the humid air thermodynamic physical model; wherein, the second state parameter is another parameter among the humid air state parameters; when the second state parameter is wet-bulb temperature, a monotonic objective function about dry-bulb temperature is constructed, and the monotonic objective function is solved based on the bisection method; when the second state parameter is relative humidity, the target saturated vapor pressure is calculated, and the saturated vapor pressure formula is numerically inversely solved to obtain the dry-bulb temperature; The moist air state parameters include second-type parameters, which are combined parameters obtained through a single main variable closed equation; when the moist air state parameters are second-type parameters, the objective evaluation strategy includes: The process involves constructing residual functions for the main variables in the second type of parameters and solving these residual functions using a one-dimensional numerical iterative algorithm to obtain the set of humid air state parameters. This includes: when the second type of parameters are a combination of wet-bulb temperature and relative humidity, constructing a residual function based on the equality of water vapor partial pressure and solving iteratively; when the second type of parameters are a combination of specific enthalpy and relative humidity, constructing a residual function based on the coupling of energy conservation and humidity definition and solving iteratively; and when the second type of parameters are a combination of specific volume and relative humidity, constructing a residual function based on the coupling of the state equation and humidity definition and solving iteratively. The moist air state parameters include third-type parameters, which are combined parameters of strongly coupled nonlinear functions of dry-bulb temperature and moisture content; when the moist air state parameters are third-type parameters, the objective evaluation strategy includes: A system of nonlinear equations containing at least two residual equations is constructed, and a multidimensional numerical iterative algorithm based on the Jacobian matrix is ​​used to jointly solve the system of nonlinear equations to obtain the dry-bulb temperature and moisture content. This process includes: when the third type of parameter is a combination of wet-bulb temperature and specific volume, constructing and solving a system of nonlinear equations based on the constraints of the dry-bulb and wet-bulb equations and the moist air state equation; and when the third type of parameter is a combination of specific enthalpy and specific volume, constructing and solving a system of nonlinear equations based on the enthalpy definition equation and the moist air state equation.

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